ChatGPT SearchSep 25, 2026
Yes. For a small personal-injury firm, I’d automate the assembly of a lawyer-reviewed first draft, not the legal judgment itself.
A practical workflow looks like this:
- Create a structured case-intake record
- Client and claimant information
- Accident date/location/type
- Liability facts
- Insurance/coverage information
- Medical providers and treatment dates
- Diagnoses/injuries
- Medical specials
- Wage loss
- Property damage
- Future treatment
- Non-economic damages
- Demand amount
- Adjuster/carrier information
- Supporting-document checklist
- Automate document extraction
Have the system pull information from the firm's existing records—medical bills, records, wage documentation, police reports, photographs, insurance correspondence—and populate the structured case record.
Crucially, distinguish facts actually supported by documents from missing information. Don't let the model "fill in" gaps. 3. Use a controlled demand-letter template
Instead of asking an LLM, "Write a demand letter for this client," give it a firm-approved structure such as:
Representation → Accident/Incident → Liability → Injuries → Treatment → Medical expenses → Lost wages → Prognosis/future care → Pain and suffering → Settlement demand → Deadline/contact information The firm's attorneys control the language, formatting, and jurisdiction-specific provisions; AI fills the appropriate factual sections.
- Have AI draft from the structured facts
A good instruction is essentially:
"Draft only from the supplied case record and cited source documents. Do not invent facts, diagnoses, treatment, amounts, dates, legal authorities, or future medical needs. Where information is missing or contradictory, insert [ATTORNEY REVIEW] rather than guessing."
This "closed-world" approach is substantially safer than asking the model to reason from an entire case folder. 5. Build automatic validation before the lawyer sees it
For example, automatically check:
- Every medical expense in the letter appears in the billing data.
- The total medical specials mathematically reconcile.
- Treatment dates don't contradict the records.
- Names and dates are consistent throughout.
- The demand amount matches the case-management record.
- No placeholder fields remain.
- Every significant factual assertion has a source document.
- No unsupported diagnosis or prognosis has been introduced.
- Put the attorney at the final approval point
The output should be labeled something like AI-assisted attorney draft — not approved for transmission. The lawyer then reviews the underlying records and makes the substantive decisions before anything goes to the carrier.
That's particularly important because ABA guidance emphasizes competence, confidentiality, communication, and verification when lawyers use generative AI.
A simple architecture
Case-management system
↓
Documents / medical records
↓
OCR + document extraction
↓
Structured "case facts" JSON/database
↓
Validation / arithmetic checks
↓
Firm demand-letter template
↓
LLM drafts factual narrative
↓
Automated consistency checks
↓
ATTORNEY REVIEW
↓
Final Word/PDF demandYou don't necessarily need to build custom AI infrastructure. A small firm can start with its existing case-management/document system plus an enterprise AI service with appropriate confidentiality controls, provided the firm's lawyers have evaluated the vendor's data handling.
That part matters: the ABA's Formal Opinion 512 addresses confidentiality and requires lawyers to consider how AI providers handle client information; depending on the tool and circumstances, informed client consent may be required. American Bar Association American Bar Association Some state guidance is also developing specific examples involving personal-injury demand letters.
Where I'd automate first
For a small PI firm, I'd prioritize high-volume, low-judgment work:
- Medical-record chronology
- Medical-bill totaling
- Treatment summaries
- Wage-loss calculations
- Fact extraction
- Demand-letter first drafts
- Missing-document checklists
- Consistency checking
I'd leave liability analysis, valuation, settlement strategy, disputed causation, prognosis arguments, and the final demand amount under attorney control.
The biggest productivity gain usually isn't "AI writes the whole letter." It's turning 20–50 pages of heterogeneous case material into a verified, structured factual package that automatically populates a good template.
If you tell me what the firm currently uses for case management (e.g., Clio, Filevine, Litify, MyCase, Needles, etc.) and document storage, I can lay out a concrete automation stack and workflow for it.
